Forensic Feature-Comparison Expertise: Statistical Learning Facilitates Visual Comparison Performance

Forensic feature-comparison examiners in select disciplines are more accurate than novices when comparing samples of visual evidence. This article examines a key cognitive mechanism that may contribute to this superior visual comparison performance: the ability to learn how often stimuli occur in the environment (distributional statistical learning). We examined the relationship between distributional learning and visual comparison performance and the impact of training on the diagnosticity of distributional information in visual comparison tasks. We compared performance between novices given no training (uninformed novices; n = 32), accurate training (informed novices; n = 32), or inaccurate training (misinformed novices; n = 32) in Experiment 1 and between forensic examiners (n = 26), informed novices (n = 29), and uninformed novices (n = 27) in Experiment 2. Across both experiments, forensic examiners and novices performed significantly above chance in a visual comparison task in which distributional learning was required for high performance. However, informed novices outperformed all participants, and only their visual comparison performance was significantly associated with their distributional learning. It is likely that forensic examiners' expertise is domain specific and doesn't generalize to novel visual comparison tasks. Nevertheless, diagnosticity training could be critical to the relationship between distributional learning and visual comparison performance. Public Significance Statement This study suggests that the ability to visually compare (or "match") complex visual patterns can be improved by training individuals to use their knowledge of what features are rare or common in these patterns. This research has important practical implications for the performance of forensic feature-comparison examiners in the criminal justice system.